Inferential Analysis: From Sample to Population

Inferential analysis is used to generalize the results obtained from a random (probability) sample back to the population from which the sample was drawn. This analysis is only required when: a sample is drawn by a random procedure; and the response rate is very high. Hence, this type of analysis is not appropriate when: non-probability methods of selection are used; the response rate is less than, say, 85 per cent, unless independent evidence is available to indicate that the sample is reasonably representative; and the data are obtained from a population. There are some technical concepts here that will be explained in due course. However, I have made these assertions at the beginning because there is a great deal of confusion in the literature about when inferential analysis should be used in social research, with the result that it is often applied inappropriately and unnecessarily. There are a number of ...

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What basic knowledge and skills do novice researchers in social science require? How can students be helped to over-come ‘symbol phobia’ or ‘figure blindness’? This generous and constantly insightful book is designed for social researchers who need to know what procedures to use under what circumstances, in practical research projects. It accomplishes this without requiring an in-depth understanding of statistical theory, but also avoids both trivializing procedures or resorting to ‘cookbook’ techniques. Among the key features of the book are: 1) accessibility - organization of the wide, often bewildering array of methods of data analysis into a coherent and user-friendly scheme of classification: types of analysis and levels of measurement; 2) demystification - the first chapter unpacks commonly taken-for-granted concepts such as ‘analysis’, ‘data’ and ‘quantitative’; 3) location of methods in real research problems. The book is an introduction to the theory and practice of quantitative methods.